Fig 9. Learning rate calibration affects both the transient and the steady-state performance of closed-loop BMI decoders with discrete spiking activity.

Figure conventions are the same as Fig 8. (A) The evolution of the decoded trajectory across time under different learning rates r. Each color corresponds to one learning rate. As in Fig 8, the decoder is fixed after a given adaptation time is completed (as noted on each row). The fixed decoder is then used to generate the displayed trajectories. The decoding performance is unstable when the learning rate is large (r = 10⁻³), i.e., the performance widely oscillates. (B) RMSE of the decoded trajectory under different learning rates for different adaptation times. RMSE is computed for a fixed decoder that was obtained by stopping the adaptation at various times (different colors). RMSE converges faster as the learning rate is increased (r = 10⁻⁷ to 10⁻⁵, for example). However, if the learning rate is selected too large (r = 10⁻³), RMSE oscillates without converging to a stable number. These results again demonstrate the importance of calibrating the learning rate for fast convergence and accuracy of decoding.